ccq1 commented 2 days ago

Checked other resources

[X] I added a very descriptive title to this issue.
[X] I searched the LangChain documentation with the integrated search.
[X] I used the GitHub search to find a similar question and didn't find it.
[X] I am sure that this is a bug in LangChain rather than my code.
[X] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

Example Code

prompt_system = """ You are a transcription engineer. I will provide you with some code, and you need to transcribe it into Python. You must use the check_code_syntax tool. If the last tool execution resulted in a code error, you need to correct the code based on the error message and then invoke the check_code_syntax tool again to see if it can compile successfully. If it passes the check, meaning the return value is_success is True, you should output TASK_END + the corrected code. """ tools = [check_code_syntax] def create_agent(): prompt = ChatPromptTemplate.from_messages( [ ( "system", prompt_system, ), MessagesPlaceholder(variable_name='user') ] ) prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools])) agent = prompt | llm.bind_tools(tools) return agent

rewrite_agent = create_agent()

def should_continue(state:MessagesState) -> bool: """Return the next node to execute.""" last_message = state["messages"][-1]

If there is no function call, then we finish

if not last_message.tool_calls:
    return END
# Otherwise if there is, we continue
args = last_message.tool_calls[0]['args']
if not isinstance(args,dict):
    args = json.loads(args)
return "tools"

def chatbot(state: State): return [rewrite_agent.invoke(state["messages"])]

workflow = StateGraph(MessagesState) workflow.add_node('tools', ToolNode(tools)) workflow.add_node('rewrite_agent', chatbot) workflow.add_edge(START, 'rewrite_agent') workflow.add_edge('tools','rewrite_agent') workflow.add_conditional_edges('rewrite_agent',should_continue,['tools',END])

graph = workflow.compile()

Error Message and Stack Trace (if applicable)

[Error Message, I add some print for debug]

================================ Human Message =================================

code is def add(a, b): prnt(a+b) add(3, 5)

_dict {'content': '', 'refusal': None, 'role': 'assistant', 'function_call': None, 'tool_calls': [{'id': '0192c19eef3bb5786799c95962fe59d0', 'function': {'arguments': '{"code": "def add(a, b):\n prnt(a+b)\nadd(3, 5)", "language": "python"}', 'name': 'check_code_syntax'}, 'type': 'function'}]} role assistant name None id None raw_tool_call {'id': '0192c19eef3bb5786799c95962fe59d0', 'function': {'arguments': '{"code": "def add(a, b):\n prnt(a+b)\nadd(3, 5)", "language": "python"}', 'name': 'check_code_syntax'}, 'type': 'function'} toolcalls [] ================================== Ai Message ================================== Tool Calls: check_code_syntax (0192c19eef3bb5786799c95962fe59d0) Call ID: 0192c19eef3bb5786799c95962fe59d0 Args: code: def add(a, b): prnt(a+b) add(3, 5) language: python ================================= Tool Message ================================= Name: check_code_syntax

FalseTraceback (most recent call last): File "/tmp/tmp4ljh8rbe", line 3, in add(3, 5) File "/tmp/tmp4ljh8rbe", line 2, in add prnt(a+b) ^^^^ NameError: name 'prnt' is not defined. Did you mean: 'print'?

_dict {'content': "It looks like there's a typo in the code. The function prnt should be print. Let me correct that and check the code again.\n", 'refusal': None, 'role': 'assistant', 'function_call': None, 'tool_calls': [{'id': '0192c19efcad36cfccc5be45121408cf', 'function': {'arguments': '"{\"code\": \"def add(a, b):\\n print(a+b)\\nadd(3, 5)\", \"language\": \"python\"}"', 'name': 'check_code_syntax'}, 'type': 'function'}]} role assistant name None id None raw_tool_call {'id': '0192c19efcad36cfccc5be45121408cf', 'function': {'arguments': '"{\"code\": \"def add(a, b):\\n print(a+b)\\nadd(3, 5)\", \"language\": \"python\"}"', 'name': 'check_code_syntax'}, 'type': 'function'} toolcalls [] Traceback (most recent call last): File "/home/son1enardo/nj_rewrite/re_write_agent.py", line 167, in for chunk in graph.stream({"messages": [inputmessage]}, File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langgraph/pregel/init.py", line 1298, in stream for in runner.tick( File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langgraph/pregel/runner.py", line 56, in tick run_with_retry(t, retry_policy) File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langgraph/pregel/retry.py", line 29, in run_with_retry task.proc.invoke(task.input, config) File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langgraph/utils/runnable.py", line 409, in invoke input = context.run(step.invoke, input, config, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langgraph/utils/runnable.py", line 183, in invoke ret = context.run(self.func, input, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/son1enardo/nj_rewrite/re_write_agent.py", line 141, in chatbot return [rewrite_agent.invoke(state["messages"])] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langchain_core/runnables/base.py", line 3024, in invoke input = context.run(step.invoke, input, config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langchain_core/runnables/base.py", line 5354, in invoke return self.bound.invoke( ^^^^^^^^^^^^^^^^^^ File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 286, in invoke self.generate_prompt( File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 786, in generate_prompt return self.generate(prompt_messages, stop=stop, callbacks=callbacks, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 643, in generate raise e File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 633, in generate self._generate_with_cache( File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 851, in _generate_with_cache result = self._generate( ^^^^^^^^^^^^^^^ File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langchain_openai/chat_models/base.py", line 690, in _generate return self._create_chat_result(response, generation_info) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langchain_openai/chat_models/base.py", line 727, in _create_chat_result message = _convert_dict_to_message(res["message"]) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langchain_openai/chat_models/base.py", line 138, in _convert_dict_to_message return AIMessage( ^^^^^^^^^^ File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langchain_core/messages/ai.py", line 179, in init super().init(content=content, kwargs) File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langchain_core/messages/base.py", line 76, in init super().init(content=content, *kwargs) File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/langchain_core/load/serializable.py", line 111, in init super().init(args, **kwargs) File "/home/son1enardo/miniconda3/envs/langgraph/lib/python3.11/site-packages/pydantic/main.py", line 212, in init validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ pydantic_core._pydantic_core.ValidationError: 1 validation error for AIMessage tool_calls.0.args Input should be a valid dictionary [type=dict_type, input_value='{"code": "def add(a, b):..., "language": "python"}', input_type=str] For further information visit https://errors.pydantic.dev/2.9/v/dict_type

Description

The LLM returns two AIMessage objects, but there is inconsistency in how tool_call.0.args is structured between them. In the first AIMessage, the tool_call.0.args is returned correctly as expected, like this: {'arguments': '{"code": "def add(a, b):\\n prnt(a+b)\\nadd(3, 5)", "language": "python"}', 'name': 'check_code_syntax'} However, in the second AIMessage, the tool_call.0.args is returned as a nested, escaped string, like this: {'arguments': '"{\\"code\\": \\"def add(a, b):\\\\n print(a+b)\\\\nadd(3, 5)\\", \\"language\\": \\"python\\"}"'} This discrepancy occurs without any additional processing on the returned results, and the second message causes issues due to the additional escaping, which requires further parsing to get the correct dictionary structure.

System Info

System Information

OS: Linux OS Version: #1 SMP Fri Mar 29 23:14:13 UTC 2024 Python Version: 3.11.10 (main, Oct 3 2024, 07:29:13) [GCC 11.2.0]

Package Information

langchain_core: 0.3.10 langchain: 0.3.1 langchain_community: 0.3.1 langsmith: 0.1.129 langchain_elasticsearch: 0.3.0 langchain_experimental: 0.3.2 langchain_ollama: 0.2.0 langchain_openai: 0.2.2 langchain_text_splitters: 0.3.0 langgraph: 0.2.34

Optional packages not installed

langserve

Other Dependencies

aiohttp: 3.10.8 async-timeout: Installed. No version info available. dataclasses-json: 0.6.7 elasticsearch[vectorstore-mmr]: Installed. No version info available. httpx: 0.27.2 jsonpatch: 1.33 langgraph-checkpoint: 2.0.1 numpy: 1.26.4 ollama: 0.3.3 openai: 1.50.2 orjson: 3.10.7 packaging: 24.1 pydantic: 2.9.2 pydantic-settings: 2.5.2 PyYAML: 6.0.2 requests: 2.32.3 SQLAlchemy: 2.0.35 tenacity: 8.5.0 tiktoken: 0.7.0 typing-extensions: 4.12.2

ccq1 commented 2 days ago

This is a potential solution to the issue.

keenborder786 commented 1 day ago

@ccq1, their were couple of issues with your code which I have corrected, please have a look at below update code


from langchain_core.tools import tool
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai import ChatOpenAI
from langgraph.constants import START, END
from langgraph.graph import StateGraph, MessagesState
from langgraph.prebuilt import ToolNode
import json

llm = ChatOpenAI()

@tool
def check_code_syntax(code:str) -> str: # your tool
   """Check Code Syntax"""
   # assuming this tool does something
   return code

prompt_system = """
You are a transcription engineer. I will provide you with some code,
and you need to transcribe it into Python. You must use the check_code_syntax tool.
If the last tool execution resulted in a code error, you need to correct the code based on the error
message and then invoke the check_code_syntax tool again to see if it can compile successfully.
If it passes the check, meaning the return value is_success is True, you should output TASK_END + the corrected code.
"""

tools = [check_code_syntax]

def create_agent():
    prompt = ChatPromptTemplate.from_messages(
        [
            ("system", prompt_system),
            MessagesPlaceholder(variable_name='user')
        ]
    )
    agent = prompt | llm.bind_tools(tools) # your prompt does not have {tool_name variable therefore no need to bind their}
    return agent

rewrite_agent = create_agent()

def should_continue(state: MessagesState) -> bool:
    """Return the next node to execute."""
    last_message = state["messages"][-1]

    # If there is no function call, then we finish
    if not last_message.tool_calls:
        return END
    # Otherwise if there is, we continue
    args = last_message.tool_calls[0]['args']
    if not isinstance(args, dict):
        args = json.loads(args)
    return "tools"

def chatbot(state: MessagesState):
    return {'messages': rewrite_agent.invoke(state["messages"])} # This should return in form MessageState

workflow = StateGraph(MessagesState)
workflow.add_node('tools', ToolNode(tools))
workflow.add_node('rewrite_agent', chatbot)
workflow.add_edge(START, 'rewrite_agent')
workflow.add_edge('tools', 'rewrite_agent')
workflow.add_conditional_edges('rewrite_agent', should_continue, ['tools', END])

graph = workflow.compile()
graph.invoke({"messages": [('human', 'Please check the following code for me using `check_code_syntax`: lambda x:x')]}) # graph should be called rather the agent in the graph

langchain-ai / langchain

[AIMessage]tool_calls.0.args is not always returned as a valid dictionary #27632